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Understanding MEC empowered vehicle task offloading performance in 6G networks

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Abstract

The sixth-generation (6G) vehicular networks are expected to be much more large-scaled, heterogeneous, dynamic and intelligent, and are expected to meet diverse Quality of Service (QoS) and Quality of Experience (QoE) requirements from vehicular applications. This paper aims to quantitatively investigate the capability of the MEC-Cloud orchestration paradigm in provisioning heterogeneous and priority 6G-V2X (Vehicle-to-Everything) service to vehicles. We develop a scalable analytic model for capturing the 6G V2X service process, in which a latency-sensitive vehicle task can dynamically migrate between MEC servers due to vehicle mobility. Formulas for calculating performance metrics, including task rejection probability and mean task response delay, are derived. Simulation results are combined with the numerical solution to demonstrate the approximate accuracy of the model and metric formulas. Numerical analysis is applied to illustrate the impact of various parameters on the 6G V2X service performance.

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Acknowledgements

The work was supported in part by the Fundamental Research Funds for Central Universities under Grant 2020YJS041 and in part by the National Natural Science Foundation of China under Grant U1836105.

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Authors and Affiliations

  1. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China

    Lili Jiang, Xiaolin Chang & Jing Bai

  2. Department of Computer Science, Ryerson University, Toronto, Canada

    Jelena Mišić & Vojislav B. Mišić

Authors
  1. Lili Jiang
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  2. Xiaolin Chang
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  3. Jelena Mišić
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  4. Vojislav B. Mišić
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  5. Jing Bai
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Correspondence to Xiaolin Chang.

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Jiang, L., Chang, X., Mišić, J. et al. Understanding MEC empowered vehicle task offloading performance in 6G networks. Peer-to-Peer Netw. Appl. 15, 1090–1104 (2022). https://doi.org/10.1007/s12083-021-01285-1

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